論文 - 大槻　知明

Deep Reinforcement Learning Aided Online Trajectory Optimization of Cellular-Connected UAVs with Offline Map Reconstruction

Hao Q., Zhao H., Huang H., Gui G., Ohtsuki T., Adachi F.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

To reduce the outage of the connection between unmanned aerial vehicles (UAVs) and cellular networks in complex real-time channel state, and reduce the energy consumption of UAV during flight mission, an online trajectory optimization scheme of UAV based on outage probability knowledge map reconstruction is proposed. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers. The UAV first samples sparsely from the target area and calculates the outage probability of the sampling point, and then uses the Kriging algorithm to reconstruct the outage probability knowledge map. Based on the reconstructed outage probability knowledge map, with the goal of minimizing the energy consumption of UAV task execution, the UAV trajectory optimization problem is established, and a trajectory optimization algorithm based on deep reinforcement learning (DRL) is proposed to solve it. Numerical results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

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Semi-Supervised Malware Traffic Classification Via Consistency Regularization and Pseudo-Labeling

Jiao Q., Zhang X., Zhang W., Miao F., Ohtsuki T., Adebisi B., Wang Y., Gui G.

International Conference on Communication Technology Proceedings ICCT    1008 - 1012 2023年

ISSN  25767844

　概要を見る

Malware traffic classification (MTC) technology plays a crucial role in cyber security by serving as the first step of network intrusion detection system. Recently, deep learning has been successfully introduced into the field of network intrusion detection. However, its performance relies heavily on the quantity and quality of labeled samples, which are often difficult to obtain in real-world applications. To address these challenges, we propose semi-supervised MTC method based on consistency regularization and pseudo-labeling. This method utilizes unlabeled malware traffic data to improve the classification performance of the model. Specifically, we train the model using labeled malware traffic data. Then, weak data augmentation is performed on unlabeled traffic data and pseudo labels are generated through the model. Finally, the model predicts unlabeled data after strong data augmentation and compares it with pseudo labels. Model parameters are updated using losses from labeled and unlabeled samples. Experimental results demonstrate that our proposed method outperforms other classical classification approaches, including ladder networks, virtual adversarial training (VAT), convolutional neural network (CNN), and support vector machine (SVM), when the ratios of labeled samples range from 1% to 5%.

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Radio Frequency Signal Dataset Generation Based on LTE System and Variable Channels

Zhang S., Zhang Y., Zhang X., Peng Y., Sun J., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Deep learning-based radio frequency fingerprinting (RFF) identification has the potential to enhance the security performance of the physical layer. In recent years, a number of RFF datasets have been proposed to meet the large-scale data requirements for deep learning. However, these datasets are collected from similar channel environments and only contain receiver data. This paper employs different software radio peripherals to generate radio signals. Hence, it is able to adjust the signal's parameters, such as frequency band, modulation style, antenna gain, etc. In this paper, we propose a radio frequency signal dataset based on LTE system and variable channels to more properly characterize the generated signals in the real world. We collect signals at transmitters and receivers to construct the RFF dataset. Moreover, we confirm the dataset's dependability using various machine learning and deep learning methods. The dataset and reproducible code of this paper can be downloaded from GitHub11GitHub link: https://github.com/njuptzsp/XSRPdataset.

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Radio Frequency Fingerprint Identification Based on Length-Robust Representation and Incremental Learning

Shi F., Feng Z., Miao F., Fu X., Adebisi B., Ohtsuki T., Sari H., Gui G.

International Conference on Communication Technology Proceedings ICCT    236 - 240 2023年

ISSN  25767844

　概要を見る

Radio frequency fingerprinting identification (RFFI) is a technology that uses signal processing techniques to extract the characteristics of collected wireless signals to enable the individual identification of devices. Over the past few years, deep learning (DL) has made great achievements in the field of signal identification, and the research on the RFFI methods has made great progress. To address the issue of fixed-size input data required by many neural networks, such as convolutional neural network (CNN) and multilayer perceptron (MLP), this paper proposes a transformation method that converts variable-length signals into contour stella constellation (CSC). In addition, in the most DL-based the RFFI methods, a global analysis of the entire radio frequency signal is required, which demands a large amount of calculation and is easily affected by changes in the propagation environment. We propose an incremental learning (IL)-based the RFFI approach, which enables dynamic updating of the model and improves its identification and generalization capabilities. Experimental results show that our proposed method shows excellent performance in processing data streams in real time and the identification accuracy finally can reach 89.14%. The codes of this paper can be downloaded from https://github.com/sfuaena/CSC-KD.

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Non-Contact Blood Pressure Estimation Using Accurate Cardiac Movements Extracted by Hidden Semi-Markov Model

Wang S., Boazizi M., Ohtsuki T.

IEEE International Conference on Communications 2023-May   5508 - 5514 2023年

ISSN  15503607

　概要を見る

This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is 4.27± 5.84 mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is 6.63± 8.95 mmHg.

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NASEI: Neural Architecture Search-Based Specific Emitter Identification Method

Huang Y., Zhang X., Wang Y., Jiao D., Gui G., Ohtsuki T.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, these methods highly rely on expert experience to design network structures. These hand-designed fixed network structures lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) can be seen as a subdomain of automatic machine learning (AutoML), which can automatically adjust network structure and parameters according to a specific task. In this paper, we propose a neural architecture search-based SEI method, which can achieve an efficient search of the architecture with the use of a gradient descent algorithm. Experimental results show that the proposed NASEI method both improves the accuracy and reduces the parameter quantity when compared with state-of-the-art methods. Code available at https://github.com/huangyuxuan11/NASEI.git.

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Multilayer Attention Mechanism for Change Detection in SAR Image Spatial-Frequency Domain

Ma L., Wang L., Zhao C., Jiahui E., Ohtsuki T.

Proceedings International Conference on Image Processing Icip    2110 - 2114 2023年

ISSN  15224880

　概要を見る

Change detection based on synthetic aperture radar (SAR) images is a challenging task in the field of remote sensing image analysis due to the influence of noise and the lack of labeled data. In this paper, we propose a new unsupervised change detection algorithm based on deep learning, which explores the spatial and frequency domain features of SAR images in parallel to improve detection performance. Our proposed method first obtains pseudo-labels by clustering and then combines them with neural networks for unsupervised detection. To reduce the impact of noise and improve sensitivity to changes, we integrate an attention mechanism (AM) into the network. We also use complementary features to integrate the spatial and frequency domain features. These complementary features include a multi-regional feature weighted by channel-spatial AM and a deep feature filtered out by a gated linear unit (GLU). Experimental results demonstrate that the proposed method improves the detection accuracy.

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K-Means Clustering-Aided Dynamic Multi-Cell Optimization Algorithm for HAPS

Dai W., Yang S., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    5635 - 5640 2023年

ISSN  23340983

　概要を見る

High Altitude Platform Station (HAPS), functioning as a flying base station (BS) positioned in the stratosphere, has gradually captured more interest in the field of mobile communications. HAPS holds the potential to deliver extensive coverage areas and resilient networks in the face of disasters. Using beamforming techniques, a HAPS can configure multiple cells within its coverage area to serve a large user population. However, the position of HAPS and the distribution of user equipment (UE) changes with time, resulting in changes in the relative position between HAPS and UEs. Consequently, the maximum throughput of UE decreases and some UEs can suffer network outages. A previously proposed method treats this as an optimization problem and tries to maximize throughput based on an objective function. However, the computational complexity is still too high for real-time control. To help beams respond faster to position changes, some AI-based methods are proposed but they cannot guarantee that low throughput UEs are well minimized. In this paper, we propose a K-means clustering-aided particle swarm optimization (PSO) algorithm that can adjust the cell configuration to minimize the number of low throughput UEs. Taking advantage of K-means clustering, this method redefines the search range for each parameter to help PSO quickly converge to the optimum. By running simulations using realistic UE distributions in some cities, we demonstrate the superiority of our proposed method in terms of computational complexity and ability to reduce low throughput UEs.

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Heterogeneous Image Change Detection Based on Deep Image Translation and Feature Refinement-Aggregation

Zhao T., Wang L., Zhao C., Liu T., Ohtsuki T.

Proceedings International Conference on Image Processing Icip    1705 - 1709 2023年

ISSN  15224880

　概要を見る

Remote sensing change detection (CD) has been widely studied, and the CD of heterogeneous images based on cross-sensor acquisition has significant research significance. However, the scarcity of heterogeneous data and the difficulty in obtaining high-quality change maps remain significant challenges. To address these issues, we propose a deep image translation-based feature refinement-aggregation change detection network (FRAN) designed for heterogeneous images, such as optics and SAR images. First, we use data augmentation to increase the number of available images and a no-independent-component-for-encoding GAN (NICE-GAN) to translate the features from the optical domain to the SAR image domain, enabling direct comparison of images from different domains. Finally, we introduce feature refinement module and feature aggregation module to extract more accurate change information and obtain an accurate change region. Our experiments on two public datasets demonstrate that the proposed FRAN's re-detection accuracy is superior to that of four other heterogeneous detection methods.

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HAPS Trajectory Optimization Based on Throughput Heat Map

Mori T., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    7291 - 7296 2023年

ISSN  23340983

　概要を見る

High altitude platform station (HAPS) is a platform that provides direct communication to wireless devices such as smart phones by equipping unmanned aerial vehicles (UAVs) flying in the stratosphere with cellular base station functions. In this paper, we propose a HAPS trajectory optimization method based on a throughput heat map, where the HAPS trajectory is designed considering the trajectory constraints of HAPS as an aircraft. The HAPS trajectory is optimized according to the throughput characteristics to be improved, such as the average throughput of each UE (user equipment). Computer simulations using actual UE distributions for Tokyo, Osaka, and Nagoya showed that the sub-optimized figure-8 trajectory improved the 50th percentile throughput by 15-16% and the 5th percentile throughput by 3-20% for each city, respectively, compared to the non-optimized circular trajectory. Trajectories aimed at improving the 50th, 75th, and 95th percentiles improved throughput characteristics around the 50th percentile, around the 75th-95th percentile, and above the 96th percentile, respectively.

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HAPS Cell Design Method for Coexistence on Terrestrial Mobile Networks

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

High-altitude platform stations (HAPSs), which directly deliver communication services to smartphones on the ground, are attracting much attention as novel mobile communication platforms for ultrawide coverage areas. A multi-cell configuration is needed to enhance communication capacity in wide areas. Assuming the use of a phased-array antenna, we proposed an optimization method for controlling antenna parameters, such as beamwidth and beam direction, to improve spectral efficiency. HAPSs are also promising disaster-resilient networks, as they can recover coverage immediately when coverage holes appear due to the breaking down of terrestrial base stations (BSs). Users can recover connections immediately during disasters through HAPSs that use the same frequency band as terrestrial networks. However, in this scenario, HAPSs become a source of interference in terrestrial networks, so network operators should avoid affecting existing terrestrial networks through HAPSs. However, cell optimization in this coexistence scenario between HAPSs and terrestrial networks has not been investigated thoroughly. In this paper, we propose a design scheme that optimizes antenna parameters for each cell to maximize HAPS coverage in this coexistence scenario using a genetic algorithm (GA). The proposed method can effectively protect terrestrial networks through optimization by setting the initial direction of each beam to avoid terrestrial BSs. Using this scheme, the GA can start optimization with candidate combinations that satisfy the interference constraints of terrestrial networks, which can expedite optimization for better coverage. Simulation results show that the proposed scheme can achieve better coverage than the conventional scheme, which only controls beam direction. Furthermore, they show that the initial beam direction setting effectively reduces the number of performed combinations required for GA to converge.

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Dynamic Antenna Control for HAPS Using Mean Field Reinforcement Learning in Multi-Cell Configuration

Yang S., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications 2023-May   5670 - 5676 2023年

ISSN  15503607

　概要を見る

In this paper, we propose a Mean Field reinforcement learning (MFRL) method for dynamic antenna control in High-Altitude-Platform-Station (HAPS) communication system with Multi-Cell Configuration. HAPS works at stratospheric altitudes of about 20 km to provide an ultra-wide coverage area. However, the wind pressure caused HAPS movement leads to the degradation of users' throughput. Considering the multi-antenna arrays in the HAPS, to find the optimal antenna parameters of all antenna arrays for reducing the number of low-throughput users, we formulate the antenna control problem into stochastic game equilibrium. Usually, solving the stochastic to find the equilibrium needs very high computation complexity to calculate the transition probability for getting the Q-value under a certain state and action. Therefore, we use the reinforcement learning (RL) named Deep Q-Network (DQN) to learn the transition probability and predict the Q-value according to the reward fed backed from the environment. Besides, we employ the Mean field Game theory in conjunction with RL during the training phase of DQN to reduce the complexity of the interactions among agents. To evaluate the proposed method, we compare the proposed method with a genetic algorithm (GA) named Particle Swarm Optimization (PSO), Q-learning, Fuzzy Q-learning, and conventional DQN under four realistic user distribution scenarios. The simulation results show that the proposed method achieves comparable throughput performance with a high convergence rate.

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Depression Detection via User Behavior and Tweets

Ji N., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    1985 - 1990 2023年

ISSN  23340983

　概要を見る

Depression is a common mental illness and the second leading cause of disability worldwide. Traditional depression diagnosis requires communication with patients and subjective cooperation of patients, which consumes a lot of manpower, material resources, and time costs. With the accumulation of user data in social media and the development of natural language processing, computer-aided diagnosis is realized, better and objective analysis is provided, and a new idea for the diagnosis of depression is provided. We propose a multimodal model based on EmoBERTa and Transformer and a text preprocessing method for a specific pre-trained model and application context. We use user behavior information and past tweets to detect whether a user has depression. Our model achieves state-of-the-art performance on a publicly available multimodal Twitter dataset.

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Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

He Z., Yin J., Wang Y., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 4 ） 1898 - 1909 2022年12月

　概要を見る

With the ubiquitous deployment and applications of Internet of Things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target's internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.

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Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

Shi D., Li L., Ohtsuki T., Pan M., Han Z., Poor H.V.

IEEE Transactions on Mobile Computing （IEEE Transactions on Mobile Computing）  21 （ 12 ） 4426 - 4438 2022年12月

ISSN  15361233

　概要を見る

Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.

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Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

D. Shi, L. Li, T. Ohtsuki, M. Pan, Z. Han and H. V. Poor

in IEEE Transactions on Mobile Computing 21 （ 12 ） 4426 - 4438 2022年12月

研究論文（学術雑誌）, 共著

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

Endo K., Yamamoto K., Ohtsuki T.

Sensors （Sensors）  22 （ 23 ）  2022年12月

ISSN  14248220

　概要を見る

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

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Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

Chen R., Li L., Ma Y., Gong Y., Guo Y., Ohtsuki T., Pan M.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 18 ） 17403 - 17416 2022年09月

　概要を見る

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants' location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries' prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

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Wi-Fi-Based Fall Detection Using Spectrogram Image of Channel State Information

Nakamura T., Bouazizi M., Yamamoto K., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 18 ） 17220 - 17234 2022年09月

　概要を見る

Wi-Fi channel state information (CSI)-based fall detection systems have a great potential compared with other alternatives since they are nonintrusive and nonspace limited. However, in the conventional work on Wi-Fi CSI-based fall detection, a phenomenon is commonly observed: the classification performance degrades when data in different environments are used for learning and testing. Nonetheless, when the signal-to-noise-power ratio (SNR) is small, the conventional methods cannot capture features of motion and cannot segment signals accurately. Therefore, there is a need to address these problems in order to build a robust fall detection system. In this article, we propose a spectrogram-image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding-time window, and then the classifier detects fall by using the spectrogram image generated from the segmented CSI. We use a pretrained convolutional neural network (CNN) optimized for binary classification of the spectrogram images of the fall and nonfall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing. As a result, we confirmed that our proposed method outperforms the conventional one and reaches over 0.92 accuracy. In addition, compared with the conventional method, the fall detection performance of our method does not degrade even when using different environment data for learning and testing.

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Sub-GHz Two-Way Ranging Based on Phase Detection for Remote Keyless Entry Systems

Seto I., Otaka S., Yoshida H., Nonin K., Nishikawa M., Kato T., Nito Y., Ishiwata H., Otsuki T.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  71 （ 9 ） 9705 - 9720 2022年09月

ISSN  00189545

　概要を見る

Remote keyless entry systems have penetrated the vehicular market due to its convenience. However, its vulnerability against relay attacks exploiting the wireless channel is a serious threat. One of the prominent countermeasures is radio ranging between the key fob and the vehicle. Taking into account radio regulations, usability and power consumption among other things, a suitable approach is necessary to devise. This article proposes sub-GHz two-way ranging based on phase detection which offers high tolerance against reference clock offset between a pair of transceiver devices and a novel compensation scheme against multipath effects. We present the operation principle and theoretical performance under a two-path model of radio propagation and additive white Gaussian noise. In field experiments with prototype devices, the ranging accuracy is validated. We show that the proposed ranging produces sufficient performance for remote keyless entry systems, confirming if the key fob is within a few meters of the vehicle.

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